waifu-diffusion VS stable-diffusion

It's slander to say that the WD team is milking donations in the same way UD is. They already have a public working model which they are continuing to train. The WD team has also implemented features like aspect ratio bucketing into their custom trainer. If they were really milking the project for donations they would have just used the base compvis trainer it was forked from.

Use one of the original SD repos, or the code for Waifu Diffusion, or the Smirkingface refactor.

My model is based off waifu-diffusion.

waifu diffusion (free) and NovelAI (paid afaik) is what I know

This is the one I use: https://github.com/harubaru/waifu-diffusion

This is called textual inversion which use many images and a small group of promtps to describe a single element, and there are two methods: with frozen model and with unfrozen model. The first one just creates a new “word” to “guide” the model, in other words, a word that means the same as a chunk of a prompt. The resulting data is a tiny file. The second one (better known dreamboth training) adds new data to de model, so it can copy the character’s characteristics and artist style more effecively resulting in a new model file (cpkt) There are not a single tutorial, so you can find many info on r/StableDiffusion. Also, there is a third method which is based on the original training (many images and its own description) Instruction are here: Training guide I don’t know if this guide is finished though

The model is a tweaked version of waifu-diffusion using textual inversion. Still not perfect but the AI can create pretty plausible images.

Yes, you can install it with Python! https://github.com/lstein/stable-diffusion works with macOS, and you can control all the common parameter via their WebUI or CLI :)

I'm using lstein fork ("dream") and when I create an image from the terminal, it also writes back to the terminal like this:

> Whenever I ask for something like ‘seamless tiling xxxxxx’ it kinda sorta gets the idea, but the resulting texture doesn’t quite tile right.

Getting seamless tiling requires more than just have "seamless tiling" in the prompt. It also depends on if the fork you're using has that feature at all.

https://github.com/lstein/stable-diffusion has the feature, but you need to pass it outside the prompt. So if you use the `dream.py` prompt cli, you can pass it `"Hats on the ground" --seamless` and it should be perfectly tilable.

From https://github.com/lstein/stable-diffusion

Stable Diffusion Dream Script: This is the original site/script for supporting macOS. I found this soon after Stable Diffusion was publicly released and it was the site which inspired me to try out using Stable Diffusion on a mac. They have a web-based UI (as well as command-line scripts) and a lot of documentation on how to get things working.

I’m pretty sure it works for M2 as well - basically the newer ARM-based Macs. The instructions to get it working are detailed! https://github.com/lstein/stable-diffusion